In view of the problems of long data migration, high maximum occupancy rate of storage space, high error rate of transfer learning and low online probability of visited data, the historical data migration technology of domestic database based on domestic Central Processing Unit(CPU) environment is studied. Firstly, the system software and hardware are clustered and deployed in the domestic CPU environment to improve the migration rate of historical data between domestic databases. Secondly, an isolation forest model is established, and the historical data is input into the isolation forest model for trend prediction, thereby eliminating the abnormal data in the domestic database, and reducing the amount of data to be migrated. Finally, a data migration model is constructed, and an alternating optimization strategy is adopted to find the optimal solution of the model, thus completing the migration of historical data in domestic databases. The experimental results show that the data migration time of this method is 18 minutes, and the maximum occupancy rate of storage space is between 10% and 25%, the ALC(Area under the Learning Curve) index value is 0.78~0.95, and the online probability of the accessed data can always be maintained at more than 97%, proving that this method has a short data migration time, a low maximum occupancy rate of storage space, a low error rate of migration learning, and high access efficiency, demonstrating good application effects.